Is the Internet of Things just the latest buzzword, or does it actually mean something? And, if it does mean something, why does it matter? Kevin Ashton is a British technology pioneer and inventor of the term Internet of Things.

Is the Internet of Things just the latest buzzword, or does it actually mean something? And, if it does mean something, why does it matter? Kevin Ashton is a British technology pioneer and inventor of the term Internet of Things.

The healthcare industry is one such field which has been blessed with this high-tech innovation. It ensures a steady and productive interaction among devices and people to deliver healthcare solutions. At present, with the efficiency of IoT, new tools are made available for developing an integrated healthcare system which makes sure the patients are handled […]

The healthcare industry is one such field which has been blessed with this high-tech innovation. It ensures a steady and productive interaction among devices and people to deliver healthcare solutions. At present, with the efficiency of IoT, new tools are made available for developing an integrated healthcare system which makes sure the patients are handled in the best possible manner with a stern focus on improved treatment outcomes.

Thus, it is an accumulation of multiple opportunities which can be used by wellness promoters and hospitals to optimize resources in the most effective manner. Currently, a majority of hospitals are taking the help of IoT for asset management as well as to control temperature and humidity within operating rooms. Let us focus on the prime advantages of IoT market trends in the healthcare organization and understand its efficient usage:

Boosted Management of Drugs: According to IoT industry analysis statistics, the development and management of drugs are seen as a major expense for the healthcare industry. With the help of IoT processes and devices, it is quite possible to handle these costs in a better manner.

Diminished Costs: Healthcare providers can take advantage of the connectivity related to healthcare solution. This will assist in patient monitoring on a real-time basis, thus cutting down unnecessary visits to the doctor. To be precise, home care facilities which are enhanced are definitely going to cut down cost related to hospital stays as well as re-admissions.

Enhanced Patient Experience: The linkage of the health care system with the internet of things, emphasizes the need or requirement of the patient. It highlights improved accuracy in terms of diagnosis, enhanced treatment outcomes and timely intervention by physicians, which leads to accountable care that is highly valued by patients.

Improved Disease Management: It is quite crucial to note that, when patients are monitored on a regular basis with the availability of real-time data, the treatment of diseases can be managed well before the issue grows hazardous.

Improved Results of Treatment: These health care solutions with the help of cloud computing or another form of virtual infrastructure offer the medical professionals the ability to utilize real-time information for taking informed decisions. Also, it ensures that the health care provision is processed timely and treatment outcomes are upgraded.

Minimizes Errors: What does IoT do when it comes to data errors? The presence of IoT in the healthcare sector works to offer accurate data collection as well as automated workflows. Moreover, it also keeps a check on data-driven decisions to trim down wastes and reduce system costs. This is one of the shining benefits of the Internet of Things which is readily enjoyed by this sector.

Various benefits related to patient management as well as diagnosis assistance in real-time are seen as the striking benefits of this technology. Moreover, there are varied risks which develops a concern related to the proper implementation of IoT.

Will we see major shifts in the level of human effort involved in bring apps to fruition? Will companies gravitate towards favoring app developers who specialize in the IoT sector? How will existing app developers need to adapt their knowledge and skill sets? Check the Infographic!

Will we see major shifts in the level of human effort involved in bring apps to fruition? Will companies gravitate towards favoring app developers who specialize in the IoT sector? How will existing app developers need to adapt their knowledge and skill sets? Check the Infographic!

Digital technology has made it possible for us to modify their environment according to our needs and desires using the “Internet of Things”, which allows the interconnection via the Internet of computing devices embedded in everyday objects, enabling them to send and receive data. 1. What’s the “Internet of Things”, and how does it work? The Internet of […]

Digital technology has made it possible for us to modify their environment according to our needs and desires using the “Internet of Things”, which allows the interconnection via the Internet of computing devices embedded in everyday objects, enabling them to send and receive data.

1. What’s the “Internet of Things”, and how does it work?
The Internet of Things works on the networking of physical objects enabled with sensors and actuators that assemble and transfer information about these objects. The collected information is now analysed and then used in the optimisation of products and services. These devices work on semiconductors, which are microchip designs extremely efficient and work on low power in certain applications. After proper research, the company can share the data with manufacturers who work with the concept of the Internet of Things. The give-and-take of data between companies can provide a good growth opportunity for semiconductor companies.

The Internet of Things can help us know more about the environment around us. These connected devices – smart metres, electricity grids, radio-frequency identification tags, connected cars, wearable items – produce new data every day. New data, with the help of mobile and data networks, help people to work in a more practical manner and use their decision-making powers more efficiently. The Internet of Things helps us to cope with a world that has become more intelligent, instrumented and interconnected with each other.

However, the bad news is that 90 percent of data produced in the Internet of Things is never actually utilised. Yet, the concept not just brings out the value of data created, but also the intelligence required behind it.

An infographic from the insurance company AIG estimates that the IoT’s economic impact will reach $14.4 trillion by 2020, ushering in a “new economic age.”

2. How has the Internet of Things revolutionised technology?
The technological period is always in constant change, and is always advancing towards a better future. Concepts such as the Internet of Things, big data and analytics and robotics need a complete digital retraining and renovation in manufacturing and operations. Moreover, devices that communicate information has helped people to make better decisions, boost productivity, manage energy efficiently, work on better inventory management and cheaper products. Similarly, the Internet of Things has its own successful contributions to display –

a) It helps in the speedy manufacturing of new products, collecting response and further optimising production and supply chain networks. The process is highly dependent on the interconnectivity between machines, sensors and control systems.
b) IoT also works in assisting the management system by working on predictive maintenance, evaluating statistics and take measures to main dependability. Industrial management systems on a small scale can be optimised with a smart-grid system, which allows actual energy optimisation.
c) IoT and similar cloud-based GPS solutions can track individual items through “speaking” microchips and transfer important data such as identification, temperature, pressure, location and humidity among others.

3. How is the current market for the “Internet of Things”?
According to statistics, the market for IoT devices and services has now developed into a huge technology development, which was unthinkable earlier. IoT has surpassed past expectations and created new ones, as can be seen in the following points.
a) Easy availability – IoT devices and services are now much more easily available. For example, all of us know about Apple’s Health kit and Home kit developer which was part of its recent OS update. Similarly, Google worked with the Nest to develop an Internet of Things platform and apps….

Blockchain technology offers a way of recording transactions or any digital interaction in a way that is designed to be secure, transparent, highly resistant to outages, auditable, and efficient; as such, it carries the possibility of disrupting industries and enabling new business models. The technology is young and changing very rapidly; widespread commercialization is still […]

Blockchain technology offers a way of recording transactions or any digital interaction in a way that is designed to be secure, transparent, highly resistant to outages, auditable, and efficient; as such, it carries the possibility of disrupting industries and enabling new business models. The technology is young and changing very rapidly; widespread commercialization is still a few years off. Nonetheless, to avoid disruptive surprises or missed opportunities, strategists, planners, and decision makers across industries and business functions should pay heed now and begin to investigate applications of the technology.

Blockchain is a database that maintains a continuously growing set of data records. It is distributed in nature, meaning that there is no master computer holding the entire chain. Rather, the participating nodes have a copy of the chain. It’s also ever-growing — data records are only added to the chain.

A Blockchain consists of two types of elements:

Transactions are the actions created by the participants in the system.

Blocks record these transactions and make sure they are in the correct sequence and have not been tampered with.

The big advantage of Blockchain is that it’s public. Everyone participating can see the blocks and the transactions stored in them. This doesn’t mean everyone can see the actual content of your transaction, however; that’s protected by your private key.

A Blockchain is decentralized, so there is no single authority that can approve the transactions or set specific rules to have transactions accepted. That means there’s a huge amount of trust involved since all the participants in the network have to reach a consensus to accept transactions.

Most importantly, it’s secure. The database can only be extended and previous records cannot be changed (at least, there’s a very high cost if someone wants to alter previous records).

When someone wants to add a transaction to the chain, all the participants in the network will validate it. They do this by applying an algorithm to the transaction to verify its validity. What exactly is understood by “valid” is defined by the Blockchain system and can differ between systems. Then it is up to a majority of the participants to agree that the transaction is valid.

A set of approved transactions is then bundled in a block, which gets sent to all the nodes in the network. They, in turn, validate the new block. Each successive block contains a hash, which is a unique fingerprint, of the previous block.

Blockchain ensures that data has not been tampered with, offering a layer of time-stamping that removes multiple levels of human checking and makes transactions immutable. However, it isn’t yet the cure-all that some believe it to be.’

There are three types of Blockchains:

Public: a public Blockchain is a Blockchain where everyone can see all the transactions, anyone can expect their transaction to appear on the ledger and finally anyone can participate to the consensus process.

Federated: federated Blockchain don’t allow everyone to participate to the consensus process. Indeed, only a limited number of nodes are given the permission to do so. For instance, in a group of 20 pharmaceutical companies we could imagine that for a block to be valid, 15 of them have to agree. The access to the Blockchain however can be public or restricted to the participants.

Private: private Blockchains are usually used inside a company. Only specific members are allowed to access it and carry out transactions.

Blockchain technology certainly has many positive aspects, but there is also much misunderstanding and confusion regarding its nature.

Myth # 1: The Blockchain is a magical database in the cloud

The Blockchain is conceptually a flat file – a linear list of simple transaction records. “This list is ‘append only so entries are never deleted, but instead, the file grows indefinitely and must be replicated in every node in the peer-to-peer network”

Blockchain doesn’t allow you to store any type of physical information like a Word document or a pdf file. It can only provide a “proof-of-existence” the distributed ledger can only contain a code that certifies the existence of a certain document but not the document itself. The file however can be stored in “data lakes”, the access to which is controlled by the owner of the information.

Myth #2: Blockchain is going to change the world

We can use Blockchain for complex and technical transactions – such as verifying the authenticity of a diamond or the identity of a person. There is also talk of a Blockchain application for the bill of lading in trade finance, which would be revolutionary in terms of cost reduction and transaction speed.

While #Blockchain can support these cases and mitigate the risk of a fraudster tampering with the ledger, it does not eradicate the threat of fraud online and it still raises questions over confidentiality. Additionally, the use of Blockchain technology will still be inefficient for many of these cases when compared to maintaining a traditional ledger.

Myth #3: Blockchain is free

Despite the commonly held belief, Blockchain is neither cheap nor efficient to run – yet. It involves multiple computers solving mathematical algorithms to agree a final immutable result, which becomes the so-called single version of truth (SVT). Each ‘block’ in the Blockchain typically uses a large amount of computing power to solve. And someone needs to pay for all this computer power that supports the Blockchain service.

Myth #4: There is only one Blockchain

There are many different technologies that go by the name Blockchain. They come in public and private versions, open and closed source, general purpose and tailored to specific solutions.

Common denominator is that they are shore up by crypto, are distributed and have some form of consensus mechanism. Bitcoin’s Blockchain, Ethereum, Hyperledger, Corda, and IBM and Microsoft’s Blockchain-as-a-service can all be classified as Distributed Ledger Technologies.

Myth #5: The Blockchain can be used for anything and everything.

Though the code is powerful, it’s not magical. Bitcoin and Blockchain developers can be evangelical, and it’s easy to understand why. For many, the Blockchain is an authority tied to mathematics, not the government or lawyers. In the minds of some developers the Blockchain and smart contracts will one day replace money, lawyers, and other arbitration bodies. Yet the code is limited to the number of cryptocurrency transactions in the chain itself, and cryptocurrency is still far from mainstream.

Myth #6: The Blockchain can be the backbone of a global economy.

No national, or corporate entity owns or controls the Blockchain. For this reason, evangelists hope private Blockchains can provides foundational support for dozens of encrypted and trusted cryptocurrencies. Superficially, the Bitcoin Blockchain appears massive. Yet a Gartner report recently claimed the size of the Blockchain is similar in scale to the NASDAQ network. If cryptocurrency takes off, and records are generated larger, this may change. For now, though, the Blockchain network is roughly analogous to contemporary financial networks.

Myth #7: The Blockchain ledger is locked and irrevocable.

Analogous large-scale transaction databases like bank records are, by their nature, private and tied to specific financial institutions. The power of Blockchain, of course, is that the code is public, transactions are verifiable, and the network is cryptographically secure. Fraudulent transactions— double spends, in industry parlance—are rejected by the network, preventing fraud. Because mining the chain provides financial incentive in the form of Bitcoin, it is largely believed that rewriting historic transactions is not in the financial interest of participants. For now, However, as computational resources improve with time, so too does the potential for deception. The impact of future processing power on the integrity of the contemporary Blockchain remains unclear.

Myth # 8: Blockchain records can never be hacked or altered.

One of the main selling points about Blockchains is their inherent permanence and transparency. When people hear that, they often think that means that Blockchains are invulnerable to outside attacks. No system or database will ever be completely secure, but the larger and more distributed the network, the more secure it is believed to be. What Blockchains can provide to applications that are developed on top of them is a way of catching unauthorized changes to records.

Myth # 9: Blockchain can only be used in the financial sector

Blockchain started to create waves in the financial sector because of its first application, the bitcoin cryptocurrency, which directly impacted this field. Although Blockchain has numerous areas of application, finance is undeniably one of them. The important challenges that this technology brings to the financial world pushed international banks such as Goldman Sachs or Barclays to heavily invest in it. Outside the financial sector, Blockchain can and will be used in real estate, healthcare or even at a personal scale to create a digital identity. Individuals could potentially store a proof-of-existence of medical data on the Blockchain and provide access to pharmaceutical companies in exchange for money.

Myth # 10: Blockchain is Bitcoin

Since Bitcoin is more famous than the underlying technology, Blockchain, many people get confused between the two.

Blockchain is a technology that allows peer-to-peer transactions to be recorded on a distributed ledger across the network. These transactions are stored in blocks and each block is linked to the previous one, therefore creating a chain. Thus, each block contains a complete and time-stamped record of all the transactions that occurred in the network. On the Blockchain, everything is transparent and permanent. No one can change or remove a transaction from the ledger.

Bitcoin is a cryptocurrency that makes electronic payment possible directly between two people without going through a third party like a bank. Bitcoins are created and stored in a virtual wallet. Since there are no intermediaries between the two parties, no one can control the cryptocurrency. Hence, the number of bitcoins that will ever be released is limited and defined by a mathematical algorithm.

Myth # 11: Blockchain is designed for Business interactions only

Experts in Blockchain are convinced that this technology will change the world and the global economy just like dot-coms did in the early 90’s. Hence, it is not only open to big corporations; it is accessible to everyone everywhere. If all it takes is an Internet connection to use the Blockchain, one can easily imagine how many people worldwide will be able to interact with each other.

Myth # 12: Smart contracts have the same legal value as regular contracts

For now, smart contracts are just pieces of code that execute actions automatically when certain conditions are met. Therefore, they are not considered as regular contracts from a legal perspective. However, they can be used as a proof of whether or not a certain task has been accomplished. Despite their uncertain legal value, smart contracts are very powerful tools especially when combined with the internet-of-things (IoT).

IoT is bringing more and more devices (things) into the digital fold every day, which will likely make IoT a multi-trillion dollars’ industry in the near future. To understand the scale of interest in the internet of things (IoT) just check how many conferences, articles, and studies conducted about IoT recently, including a recent well […]

IoT is bringing more and more devices (things) into the digital fold every day, which will likely make IoT a multi-trillion dollars’ industry in the near future. To understand the scale of interest in the internet of things (IoT) just check how many conferences, articles, and studies conducted about IoT recently, including a recent well written article about IoT future by SAP listing 21 experts insights about IoT future. This interest has hit fever pitch point last year as many companies see big opportunity and believe that IoT holds the promise to expand and improve businesses processes and accelerate growth.

However, the rapid evolution of the IoT market has caused an explosion in the number and variety of #IoT solutions, which created real challenges as the industry evolves, mainly, the urgent need for a reliable IoT model to perform common tasks such as sensing, processing, storage, and communicating. Developing that model will never be an easy task by any stretch of the imagination; there are many hurdles and challenges facing a real reliable IoT model.

One of the crucial functions of using IoT solutions is to take advantage of IoT analytics to exploit the information collected by “things” in many ways — for example, to understand customer behavior, to deliver services, to improve products, and to identify and intercept business moments. IoT demands new analytic approaches as data volumes increase through 2021 to astronomical levels, the needs of the IoT analytics may diverge further from traditional analytics.

There are many challenges facing IoT Analytics including; Data Structures, Combing Multi Data Formats, The Need to Balance Scale and Speed, Analytics at the Edge, and IoT Analytics and AI.

Data structures

Most sensors send out data with a time stamp and most of the data is “boring” with nothing happening for much of the time. However once in a while something serious happens and needs to be attended to. While static alerts based on thresholds are a good starting point for analyzing this data, they cannot help us advance to diagnostic or predictive or prescriptive phases. There may be relationships between data pieces collected at specific intervals of times. In other words, classic time series challenges.

Combining Multiple Data Formats

While time series data have established techniques and processes for handling, the insights that would really matter cannot come from sensor data alone. There are usually strong correlations between sensor data and other unstructured data. For example, a series of control unit fault codes may result in a specific service action that is recorded by a mechanic. Similarly, a set of temperature readings may be accompanied by a sudden change in the macroscopic shape of a part that can be captured by an image or change in the audible frequency of a spinning shaft. We would need to develop techniques where structured data must be effectively combined with unstructured data or what we call Dark Data.

The Need to Balance Scale and Speed

Most of the serious analysis for IoT will happen in the cloud, a data center, or more likely a hybrid cloud and server-based environment. That is because, despite the elasticity and scalability of the cloud, it may not be suited for scenarios requiring large amounts of data to be processed in real time. For example, moving 1 terabyte over a 10Gbps network takes 13 minutes, which is fine for batch processing and management of historical data but is not practical for analyzing real-time event streams, a recent example is data transmitted by autonomous cars especially in critical situations that required a split second decision.

At the same time, because different aspects of IoT analytics may need to scale more than others, the analysis algorithm implemented should support flexibility whether the algorithm is deployed in the edge, data center, or cloud.

IoT Analytics at the Edge

IoT sensors, devices and gateways are distributed across different manufacturing floors, homes, retail stores, and farm fields, to name just a few locations. Yet moving one terabyte of data over a 10Mbps broadband network will take nine days. So enterprises need to plan on how to address the projected 40% of IoT data that will be processed at the edge in just a few years’ time. This is particularly true for large IoT deployments where billions of events may stream through each second, but systems only need to know an average over time or be alerted when a trends fall outside established parameters.

The answer is to conduct some analytics on IoT devices or gateways at the edge and send aggregated results to the central system. Through such edge analytics, organizations can ensure the timely detection of important trends or aberrations while significantly reducing network traffic to improve performance.

Performing edge analytics requires very lightweight software, since IoT nodes and gateways are low-power devices with limited strength for query processing. To deal with this challenge, Fog Computing is the champion.

Fog computing allows computing, decision-making and action-taking to happen via IoT devices and only pushes relevant data to the cloud, Cisco coined the term “Fog computing “and gave a brilliant definition for #FogComputing: “The fog extends the cloud to be closer to the things that produce and act on IoT data. These devices, called fog nodes, can be deployed anywhere with a network connection: on a factory floor, on top of a power pole, alongside a railway track, in a vehicle, or on an oil rig. Any device with computing, storage, and network connectivity can be a fog node. Examples include industrial controllers, switches, routers, embedded servers, and video surveillance cameras.”. Major benefits of using fog computing: minimizes latency, conserves network bandwidth, and addresses security concerns at all level of the network. In addition , operates reliably with quick decisions, collects and secures wide range of data, moves data to the best place for processing, lowers expenses of using high computing power only when needed and less bandwidth, and gives better analysis and insights of local data.

Keep in mind that fog computing is not a replacement of cloud computing by any measures, it works in conjunction with cloud computing, optimizing the use of available resources. But it was the product of a need to address many challenges; real-time process and action of incoming data, and limitation of resources like bandwidth and computing power, another factor helping fog computing is the fact that it takes advantage of the distributed nature of today’s virtualized IT resources. This improvement to the data-path hierarchy is enabled by the increased compute functionality that manufacturers are building into their edge routers and switches.

IoT Analytics and AI

The greatest—and as yet largely untapped—power of IoT analysis is to go beyond reacting to issues and opportunities in real time and instead prepare for them beforehand. That is why prediction is central to many IoT analytics strategies, whether to project demand, anticipate maintenance, detect fraud, predict churn, or segment customers.

Artificial Intelligence (#AI) use and improves current statistical models for handling prediction. AI will automatically learn underline rules, providing an attractive alternative to rules-only systems, which require professionals to author rules and evaluate their performance. When AI applied it provides valuable and actionable insights.

There are six types of IoT Data Analysis where AI can help:

1. Data Preparation: Defining pools of data and clean them which will take us to concepts like Dark Data, Data Lakes.

2. Data Discovery: Finding useful data in the defined pools of data

3. Visualization of Streaming Data: On the fly dealing with streaming data by defining, discovering data, and visualizing it in smart ways to make it easy for the decision-making process to take place without delay.

4. Time Series Accuracy of Data: Keeping the level of confidence in data collected high with high accuracy and integrity of data

5. Predictive and Advance Analytics: Very important step where decisions can be made based on data collected, discovered and analyzed.

6. Real-Time Geospatial and Location (logistical Data): Maintaining the flow of data smooth and under control.

But it’s not all “nice & rosy, comfy and cozy” there are challenges facing using AI in IoT; compatibility, complexity, privacy/security/safety , ethical and legal issues, and artificial stupidity.

Many IoT ecosystems will emerge, and commercial and technical battles between these ecosystems will dominate areas such as the smart home, the smart city, financials and healthcare. But the real winners will be the ecosystems with better, reliable, fast and smart IoT Analytics tools, after all what is matter is how can we change data to insights and insights to actions and actions to profit .

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https://www.datascience.us/iot-where-and-why-should-you-invest/#respondThu, 01 Jun 2017 02:08:43 +0000https://www.bisilo.com/?p=4676

Much have been written about how IoT will disrupt enterprises, but enterprises are still clueless as to how exactly they would invest in IoT. The confusion is largely owing to the actual implementation still being limited. IoT is yet to attain critical mass and become a normal phenomenon, like what a smartphone is now. However, […]

Much have been written about how IoT will disrupt enterprises, but enterprises are still clueless as to how exactly they would invest in IoT. The confusion is largely owing to the actual implementation still being limited. IoT is yet to attain critical mass and become a normal phenomenon, like what a smartphone is now. However, it is only a matter of time before it becomes so, and enterprises who have a clear cut idea of where exactly to invest, and how, can reap the early mover advantage.

The IoT Infrastructure Backbone

IoT essentially entails assigning an IP address to any “thing,” and making it thing a part of a wider ecosystem. Research major, Gartner estimates the number of physical “things” connected to the Internet to exceed 25 million by 2020, a big jump from the approximately five billion “things” connected to the Internet today.

The most obvious IoT investments are sensors, processor chips, and wi-networks, besides supporting equipment such as routers and modems. Hardware firms such as Qualcomm offer IoT chips, and networking companies such as Aruba Network, Ruckus Wireless, and a host of others, offer IoT solutions. However, not all enterprises would need to, or even have the capability to set up custom IoT implementations. They would rather deploy ready-to-go IoT solutions available in the market.

Ready to Consume Products

There is already a huge untapped market for smart devices, from smart cars that navigate automatically to smart lighting that manages energy automatically, and from smart shirts that capture body information to smart thermostats and many other things. Enterprises would obviously need to invest in “things” relevant to their business, or “things” which add value. However, even here the options are not yet clear cut, as IoT ecosystems are still its nascent stage, and yet to mature.

Usually, companies that market smart devices grab the headlines when in actuality such “marketing” companies rely on third-party firms who develop and support the products they sell.

A barometer of which IoT product or project is likely to succeed is the interest shown by investors to the device or project. For example, Google’s Nest Learning Thermostat is widely popular among investors, and backed by Google, is a safe bet for enterprises to control air conditioning and other devices and achieve optimal energy efficiency.

]]>https://www.datascience.us/iot-where-and-why-should-you-invest/feed/0AI is the Catalyst of IoThttps://www.datascience.us/ai-catalyst-iot/
https://www.datascience.us/ai-catalyst-iot/#respondThu, 04 May 2017 02:33:23 +0000https://www.datascience.us/?p=5367

The resulting transformation is ushering in a new era of how companies run their operations and engage with customers. However, tapping into the IoT is only part of the story [6]. For companies to realize the full potential of IoT enablement, they need to combine IoT with rapidly-advancing Artificial Intelligence (#AI) technologies, which enable ‘smart […]

The resulting transformation is ushering in a new era of how companies run their operations and engage with customers. However, tapping into the IoT is only part of the story [6].

For companies to realize the full potential of IoT enablement, they need to combine IoT with rapidly-advancing Artificial Intelligence (#AI) technologies, which enable ‘smart machines’ to simulate intelligent behavior and make well-informed decisions with little or no human intervention [6].

Artificial Intelligence (AI) and the Internet of Things (IoT) are terms that project futuristic, sci-fi, imagery; both have been identified as drivers of business disruption in 2017. But, what do these terms really mean and what is their relation? Let’s start by defining both terms first:

IoT is defined as a system of interrelated Physical Objects, Sensors, Actuators, Virtual Objects, People, Services, Platforms, and Networks [3] that have separate identifiers and an ability to transfer data independently. Practical examples of #IoT application today include precision agriculture, remote patient monitoring, and driverless cars. Simply put, IoT is the network of “things” that collects and exchanges information from the environment [7].

IoT is sometimes referred to as the driver of the fourth Industrial Revolution(Industry 4.0) by industry insiders and has triggered technological changes that span a wide range of fields. Gartner forecasted there would be 20.8 billion connected things in use worldwide by 2020, but more recent predictions put the 2020 figure at over 50 billion devices [4]. Various other reports have predicted huge growth in a variety of industries, such as estimating healthcare IoT to be worth $117 billion by 2020 and forecasting 250 million connected vehicles on the road by the same year. IoT developments bring exciting opportunities to make our personal lives easier as well as improving efficiency, productivity, and safety for many businesses [2].

AI, on the other hand, is the engine or the “brain” that will enable analytics and decision making from the data collected by IoT. In other words, IoT collects the data and AI processes this data in order to make sense of it. You can see these systems working together at a personal level in devices like fitness trackers and Google Home, Amazon’s Alexa, and Apple’s Siri [1].

With more connected devices comes more data that has the potential to provide amazing insights for businesses but presents a new challenge for how to analyze it all. Collecting this data benefits no one unless there is a way to understand it all. This is where AI comes in. Making sense of huge amounts of data is a perfect application for pure AI.

By applying the analytic capabilities of AI to data collected by IoT, companies can identify and understand patterns and make more informed decisions. This leads to a variety of benefits for both consumers and companies such as proactive intervention, intelligent automation, and highly personalized experiences. It also enables us to find ways for connected devices to work better together and make these systems easier to use.

This, in turn, leads to even higher adoption rates. That’s exactly why; we need to improve the speed and accuracy of data analysis with AI in order to see IoT live up to its promise. Collecting data is one thing, but sorting, analyzing, and making sense of that data is a completely different thing. That’s why it’s essential to develop faster and more accurate AIs in order to keep up with the sheer volume of data being collected as IoT starts to penetrate almost all aspects of our lives.

Our customers are already connecting thousands of intelligent devices and demanding new transparency from their vendors: They want to liberate plant floor data from operational silos and proprietary technologies. Only by doing this will they be able to adapt to the latest advances in automation, sensor technology, and machine learning… not to mention the economic […]

Our customers are already connecting thousands of intelligent devices and demanding new transparency from their vendors: They want to liberate plant floor data from operational silos and proprietary technologies. Only by doing this will they be able to adapt to the latest advances in automation, sensor technology, and machine learning… not to mention the economic imperative to get in front of tightening competition due to globalization.

To take full advantage of new technologies now hitting manufacturing – like predictive analytics, cloud computing, mobility, and collaboration – it’s critical to align operation technology goals with company IT teams. It is time to unite the Operational and the IT tribes so we can succeed together in enabling Industrie 4.0, beyond paper projects.

As I learned at Hannover, there are 5 major issues that manufacturing execs are thinking about as they work to integrate their various industrial systems with each other and with IT’s.

Here they are:

We Need Open Data Platforms

Contemporary machine learning technology means that we are getting new capabilities to see patterns and signals in data that we used to discard as noise, that we can use to improve operational efficiency.

Currently, many shop floors run legacy protocols designed for command and control; they were not designed to feed bits into a data warehouse. Without a way to collect, collate, and store data from different systems, the opportunity to harvest this data is lost.

(This is why we build switches like the IE 4000 line that support multiple protocols and standards which is the first step is bringing the data streams together. CAM for IOT Intelligence helps to connect and organize all the new streams of IOT diagnostic data for business improvement whether from sensors, machines, IP cameras, or asset and people location tags.)

We Must Move Beyond Computing Silos

The massive increase in the data flows we will be processing means that we also need to look at where and how we are doing that processing. The old client/server model, with designated machines working on particular projects, isn’t manageable and does not scale. We’ve got to get our data and our processing out of silos. We need processing at the “edge,” the interface between shop floor machines and the data centers that are archiving their information.

Many companies are organizing themselves to focus on IoT and the connectivity of their future products and services. For the IoT industry to thrive there are three categories of challenges to overcome and this is true for any new trend in technology not only IoT: technology, business and society [1, 2, 3]. Technology This part […]

Many companies are organizing themselves to focus on IoT and the connectivity of their future products and services. For the IoT industry to thrive there are three categories of challenges to overcome and this is true for any new trend in technology not only IoT: technology, business and society [1, 2, 3].

Technology

This part is covering all technologies needed to make IoT systems function smoothly as a standalone solution or part of existing systems and that’s not an easy mission, there are many technological challenges, including Security, Connectivity, Compatibility & Longevity, Standards and Intelligent Analysis & Actions [4].

Figure 1: Technological Challenges

Security: IoT has already turned into a serious security concern that has drawn the attention of prominent tech firms and government agencies across the world. The hacking of baby monitors, smart fridges, thermostats, drug infusion pumps, cameras and even the radio in your car are signifying a security nightmare being caused by the future of IoT. So many new nodes being added to networks and the internet will provide malicious actors with innumerable attack vectors and possibilities to carry out their evil deeds, especially since a considerable number of them suffer from security holes.

The more important shift in security will come from the fact that IoT will become more ingrained in our lives. Concerns will no longer be limited to the protection of sensitive information and assets. Our very lives and health can become the target of IoT hack attacks [1].

There are many reasons behind the state of insecurity in IoT. Some of it has to do with the industry being in its “gold rush” state, where every vendor is hastily seeking to dish out the next innovative connected gadget before competitors do. Under such circumstances, functionality becomes the main focus and security takes a back seat.

Connectivity: Connecting so many devices will be one of the biggest challenges of the future of IoT, and it will defy the very structure of current communication models and the underlying technologies [2]. At present we rely on the centralized, server/client paradigm to authenticate, authorize and connect different nodes in a network.

This model is sufficient for current IoT ecosystems, where tens, hundreds or even thousands of devices are involved. But when networks grow to join billions and hundreds of billions of devices, centralized systems will turn into a bottleneck. Such systems will require huge investments and spending in maintaining cloud servers that can handle such large amounts of information exchange, and entire systems can go down if the server becomes unavailable.

The future of IoT will very much have to depend on decentralizing IoT networks. Part of it can become possible by moving some of the tasks to the edge, such as using fog computing models where smart devices such as IoT hubs take charge of mission-critical operations and cloud servers take on data gathering and analytical responsibilities [5].

Other solutions involve the use of peer-to-peer communications, where devices identify and authenticate each other directly and exchange information without the involvement of a broker. Networks will be created in meshes with no single point of failure. This model will have its own set of challenges, especially from a security perspective, but these challenges can be met with some of the emerging IoT technologies such as Blockchain [6].

Compatibility and Longevity: IoT is growing in many different directions, with many different technologies competing to become the standard. This will cause difficulties and require the deployment of extra hardware and software when connecting devices.

Other compatibility issues stem from non-unified cloud services, lack of standardized M2M protocols and diversities in firmware and operating systems among IoT devices.

Some of these technologies will eventually become obsolete in the next few years, effectively rendering the devices implementing them useless. This is especially important, since in contrast to generic computing devices which have a lifespan of a few years, IoT appliances (such as smart fridges or TVs) tend to remain in service for much longer, and should be able to function even if their manufacturer goes out of service….